必要なときだけ集中：適応的ルーティングと協調グラウンディングによる学習不要の視覚グラウンディング
Focus When Necessary: Adaptive Routing and Collaborative Grounding for Training-Free Visual Grounding
複雑な高解像度画像中の細部認識が苦手なMLLMに対し、サンプルの難易度に応じて処理を適応的に割り当てる学習不要のフレームワークLazyMCoTを提案。簡単なクエリは高速に処理し、難しいケースにはモデルの注意機構と外部視覚専門家を組み合わせた2段階精緻化で正確な局所領域を生成する。
詳しい要約
1. どんなもの？
2. 先行研究と比べてどこがすごい？
3. 技術・手法の肝は？
4. どうやって有効だと検証した？
5. 議論はある？
6. 次に読むべき論文は？
※ AIが要旨から生成した要約です。正確性は原文をご確認ください。
著者: Yifan Wang, Peiming Li, Shiyu Li, Zhiyuan Hu, Xiaochen Yang, Wenming Yang, Yang Tang, Zheng Wei
分類: cs.CV, cs.CL
原文アブストラクト
While Multimodal Large Language Models (MLLMs) excel in cross-modal reasoning, they often struggle to perceive fine-grained details in complex high-resolution images. Recent training-free methods address this through image scaling and localized cropping. However, applying these manipulations indiscriminately introduces computational redundancy for simple queries and can degrade accuracy by truncating essential global context or introducing irrelevant background noise. To this end, we propose LazyMCoT, a dynamic and training-free framework that adaptively allocates visual grounding efforts based on sample difficulty. The framework features an Adaptive Routing mechanism that evaluates predictive uncertainty using first-token statistics from a single forward pass. This efficiently bypasses confident cases while ensuring the recall of difficult samples via conformal calibration. For these challenging cases, a Collaborative Grounding module integrates the inherent cross-modal attention of the model with an external visual expert through a two-stage refinement process. This refinement process generates a precise localized display to recover small or occluded targets. Extensive experiments across diverse benchmarks demonstrate that LazyMCoT rivals training-based approaches by simultaneously improving reasoning accuracy and reducing average inference latency. Our code is availble at https://github.com/TencentBAC/LazyMCoT.
